mirror of https://github.com/vladmandic/automatic
74 lines
2.5 KiB
Python
74 lines
2.5 KiB
Python
# TODO a1111 compatibility module
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import torch
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from modules import sd_samplers_common, sd_samplers_timesteps_impl, sd_samplers_compvis
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from modules.sd_samplers_cfg_denoiser import CFGDenoiser
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import modules.shared as shared
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samplers_timesteps = [
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('DDIM', sd_samplers_timesteps_impl.ddim, ['ddim'], {}),
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('PLMS', sd_samplers_timesteps_impl.plms, ['plms'], {}),
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('UniPC', sd_samplers_timesteps_impl.unipc, ['unipc'], {}),
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]
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samplers_data_timesteps = [
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sd_samplers_common.SamplerData(label, lambda model, funcname=funcname: VanillaStableDiffusionSampler(funcname, model), aliases, options)
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for label, funcname, aliases, options in samplers_timesteps
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]
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class CompVisTimestepsDenoiser(torch.nn.Module):
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def __init__(self, model, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.inner_model = model
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def forward(self, input, timesteps, **kwargs): # pylint: disable=redefined-builtin
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return self.inner_model.apply_model(input, timesteps, **kwargs)
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class CompVisTimestepsVDenoiser(torch.nn.Module):
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def __init__(self, model, *args, **kwargs):
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super().__init__(*args, **kwargs)
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self.inner_model = model
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def predict_eps_from_z_and_v(self, x_t, t, v):
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return self.inner_model.sqrt_alphas_cumprod[t.to(torch.int), None, None, None] * v + self.inner_model.sqrt_one_minus_alphas_cumprod[t.to(torch.int), None, None, None] * x_t
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def forward(self, input, timesteps, **kwargs): # pylint: disable=redefined-builtin
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model_output = self.inner_model.apply_model(input, timesteps, **kwargs)
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e_t = self.predict_eps_from_z_and_v(input, timesteps, model_output)
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return e_t
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class CFGDenoiserTimesteps(CFGDenoiser):
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def __init__(self, sampler):
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super().__init__(sampler)
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self.alphas = shared.sd_model.alphas_cumprod
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self.mask_before_denoising = True
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self.model_wrap = None
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def get_pred_x0(self, x_in, x_out, sigma):
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ts = sigma.to(dtype=int)
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a_t = self.alphas[ts][:, None, None, None]
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sqrt_one_minus_at = (1 - a_t).sqrt()
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pred_x0 = (x_in - sqrt_one_minus_at * x_out) / a_t.sqrt()
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return pred_x0
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@property
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def inner_model(self):
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if self.model_wrap is None:
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denoiser = CompVisTimestepsVDenoiser if shared.sd_model.parameterization == "v" else CompVisTimestepsDenoiser
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self.model_wrap = denoiser(shared.sd_model)
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return self.model_wrap
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VanillaStableDiffusionSampler = sd_samplers_compvis.VanillaStableDiffusionSampler
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